Learn data annotation and AI evaluation
Want to understand AI and explore related work?
Start by identifying objects, understanding text and checking AI answers.
Build your skills in data annotation and AI evaluation, step by step.
Explore data annotation
Try an example to understand the work.
Models need data.
And reliable judgment.
Annotation provides examples to learn from and evidence for evaluation. Accurate boundaries, reliable evidence and consistent judgment all contribute to data quality.
Research and practical examples
- Learning examples
- Turn objects, meaning and task requirements into learning examples.
- Human feedback
- Identify more reliable answers to provide signals for model improvement.
- Evaluation and quality
- Find errors, omissions and unreliable outputs using consistent criteria.
Work you can explore
Understand the work, then practice the judgment and guidelines it requires.
Image and video annotation
Identify objects, handle occlusion and produce complete, accurate annotations.
Explore practiceLanguage and content evaluation
Read context, extract evidence and assess content and language quality.
Explore practiceAI response evaluation
Check facts, compare answers and evaluate them against consistent criteria.
Explore practiceData in practice
OpenAI
Official opportunities
These companies are industry examples, not Annotator partners. Locations, eligibility and requirements depend on each company’s official listings.